Dynamic network equilibrium for daily activity-trip chains of heterogeneous travelers: application to large-scale networks

Halat, Hooram; Zockaie, Ali; Mahmassani, Hani S.; Xu, Xiang; Verbas, Ömer

doi:10.1007/s11116-016-9724-4

Cited by 13 publications

(14 citation statements)

References 31 publications

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“…The different values of are determined as a function of the size of the network, based on the studies of Halat et al (2016) and Verbas et al (2016). We restrict the number of inner loop iterations for each network to obtain a complete comparison of the performances of the algorithms in the same settings.…”

Section: Resultsmentioning

confidence: 99%

“…We restrict the number of inner loop iterations for each network to obtain a complete comparison of the performances of the algorithms in the same settings. The different values of are determined as a function of the size of the network, based on the studies of Halat et al (2016) and Verbas et al (2016). All the experiments are conducted on a 64-bit personal computer with a 12-core central processing unit of 2.10 GHz speed, and a memory of 128 GB.…”

Section: Resultsmentioning

confidence: 99%

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Cross‐comparison of convergence algorithms to solve trip‐based dynamic traffic assignment problems

Ameli

Lebacque

Leclercq

2020

Computer aided Civil Eng

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Solving a dynamic traffic assignment problem in a transportation network is a computational challenge. This study first reviews the different algorithms in the literature used to numerically calculate the user equilibrium (UE) related to dynamic network loading. Most of them are based on iterative methods to solve a fixed-point problem.Two elements must be computed: the path set and the optimal path flow distribution between all origin-destination pairs. In a generic framework, these two steps are referred to as the outer and the inner loops, respectively. The goal of this study is to assess the computational performance of the inner loop methods that calculate the path flow distribution for different network settings (mainly network size and demand levels). Several improvements are also proposed to speed up convergence: four new swapping algorithms and two new methods for the step size initialization used in each descent iteration. All these extensions significantly reduce the number of iterations to obtain a good convergence rate and drastically speed up the overall simulations. The results show that the performance of different components of the solution algorithm is sensitive to the network size and saturation. Finally, the best algorithms and settings are identified for all network sizes with particular attention being given to the largest scale.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Cross‐comparison of convergence algorithms to solve trip‐based dynamic traffic assignment problems

Ameli

Lebacque

Leclercq

2020

Computer aided Civil Eng

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“…in this study. It has a good performance in large-scale and congested traffic networks [20]. We developed a smart step-size selection procedure for this method in which we specialize the step size for each OD pair and decrease the step size when the OD gap is not improved compared to the previous iteration [17].…”

Section: Probabilistic Methodmentioning

confidence: 99%

“…In the literature, this classification process identifies eight categories of information about travelers: (1) choice model, (2) cost function, (3) economic attributes, (4) generalized cost function, (5) knowledge level of the network, (6) risk taking attitude, (7) social class, and (8) vehicle class. Moreover, some research uses hybrid classification (see, e.g., [19][20][21]). In this study, we attempt to define a generic cost function for users in order to represent their behavior accurately.…”

Section: Introductionmentioning

confidence: 99%

Computational Methods for Calculating Multimodal Multiclass Traffic Network Equilibrium: Simulation Benchmark on a Large-Scale Test Case

Ameli

Lebacque

Leclercq

2021

Journal of Advanced Transportation

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This study reviews existing computational methods to calculate simulation-based dynamic network equilibrium. We consider a trip-based multimodal approach for the dynamic network loading. Mode and path choices are carried out at the same level; therefore, travel times depend on the travel path and the mode attributes of travelers. This study develops a multiclass model with several parameters per class. Two different categories of algorithms (heuristic and metaheuristic) are considered in order to solve the discrete dynamic traffic assignment (DTA) problem. Finally, we analyze the equilibrium in a large-scale multimodal DTA test case (Lyon 6th + Villeurbanne) in order to investigate the performance of different optimization approaches to solve trip-based DTA. The results show that, in a multimodal and heterogeneous setting, the metaheuristic methods provide better solutions than the heuristic methods in terms of optimality and computation time. These improvements are even more significant than in a homogeneous setting.

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“…Considering the impact of the sociodemographic background, traveller heterogeneity has been considered in recent years [35,36]. Focusing on the activity-trip chain demand, a general simulation-based dynamic network equilibrium algorithm for assignment was subsequently developed [37]. Note that most studies mentioned above concentrate on model and algorithm optimization instead of evaluation of the current network.…”

Section: Tourism Traffic Planningmentioning

confidence: 99%

Sustainability and Resource Equilibrium Evaluation of a Tourism Traffic Network Based on a Tourism Traffic Matching Curve

2019

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The accompanying increased traffic due to the rapid development of tourism at present calls for the better sustainability and equilibrium of tourism and traffic resources in the future. In recent decades, considerable attention has been paid to highway network planning, while studies focusing on sustainable tourism traffic networks, especially evaluation methods of limited tourism and traffic resources, have proven less successful. Through conceptual transfer, we proposed the tourism traffic matching curve, which not only simplifies the method of sustainability and equilibrium evaluation, but also ensures that the results are more direct and effective. In the upper layer, we analyzed the main factors of tourism traffic network development, which contributed to a comprehensive benefit-oriented multi-objective model of tourism traffic network in MATLAB. In this model, the long-neglected tourism economy radiation effect was considered, and a genetic algorithm was introduced in the solution. In the lower layer, the characteristics of the tourism traffic matching curve and the two crucial equilibrium indexes, the Tourism Traffic Uniformity Coefficient (TLCU) and Curvature Coefficient (TLCC), were provided. With instances from Guangdong Province, the proposed system was proven to be more efficient and concise than other systems, which identified the need for future research on establishing effective criteria and a theoretical analysis method for dynamic adjustment.

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Dynamic network equilibrium for daily activity-trip chains of heterogeneous travelers: application to large-scale networks

Cited by 13 publications

References 31 publications

Cross‐comparison of convergence algorithms to solve trip‐based dynamic traffic assignment problems

Cross‐comparison of convergence algorithms to solve trip‐based dynamic traffic assignment problems

Computational Methods for Calculating Multimodal Multiclass Traffic Network Equilibrium: Simulation Benchmark on a Large-Scale Test Case

Sustainability and Resource Equilibrium Evaluation of a Tourism Traffic Network Based on a Tourism Traffic Matching Curve

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